How agriculture is using big data


You see, farmers more and more frequently use big data in agriculture to assess weather and soil conditions (i.a. rainfalls, water usage, pesticide and fertilizer usage, optimal harvesting time, etc.) in order to get maximum productivity from the land they cultivate. Here, technologies based on the Internet of Things (IoT) are indispensable.

Tools used in precision agriculture relies on data collection and Big Data analytics. Agricultural production practices (inputs, i.e. type and amount of seed, fertilizer and pesticides applied, yield etc.), weather patterns and soil chemistry are monitored at the farm level and collected data are stored digitally.


How will big data change agriculture?

Big data can truly revolutionize the agricultural sector only by having a cloud-based ecosystem with the right tools and software to integrate various data sources. These tools should be able to consolidate data on climate, agronomy, water, farm equipment, supply chain, weeds, nutrients, and so much more to aid the farmer make decisions.

What is the role of big data in agriculture?

Top 4 use cases for big data on the farm

  1. Feeding a growing population. This is one of the key challenges that even governments are putting their heads together to solve. …
  2. Using pesticides ethically. Administration of pesticides has been a contentious issue due to its side effects on the ecosystem. …
  3. Optimizing farm equipment. …
  4. Managing supply chain issues. …

What is AG big data?

When it comes to the year-to-date metrics, the WISeKey International Holding AG (WKEY) recorded performance in the market was -43.44%, having the revenues showcasing -35.32% on a quarterly basis in comparison with the same period year before.

How can we use data analytics in agriculture?

The opportunities of big data agriculture cannot omit

  • Increase farming productivity. Big data analytics in agriculture has already shown great results in forecasting crop production and improving crop yields.
  • Improve farming operations. …
  • Stop migration of the labor force. …
  • Reduce food waste. …
  • Attract greater investments in AgriTech. …

How is big data used in agriculture?

Big data provides farmers granular data on rainfall patterns, water cycles, fertilizer requirements, and more. This enables them to make smart decisions, such as what crops to plant for better profitability and when to harvest. The right decisions ultimately improve farm yields.

How is data analytics used in agriculture?

Data analytics can help farmers monitor the health of crops in real-time, create predictive analytics related to future yields and help farmers make resource management decisions based on proven trends. Reducing waste and improving profits.

Which of the following are the future applications of big data in the field of agriculture?

Practical Application of Big Data in AgriculturePesticides use optimization. Pesticides use is considered an issue due to its side effects on the ecosystem. … Farm equipment management. Remote management of agricultural machinery helps large farms reduce costs. … Supply chain problems management. … Yield prediction. … Food safety.

Why data is important in agriculture?

Data based decisions at the farm level can improve resource utilization and conservation practices. Similar efforts at regional level, tracking inputs per kilogram of produce or impact of production on natural resources can contribute towards long term policies for land and water conservation.

How do farmers use data?

Weather stations and sensors allow you to monitor the weather remotely. This is particularly important for farms that grow vegetables and fruits. Sensors help to prepare for a critical change in temperature and to calculate the irrigation. Also, weather data helps to predict plant diseases and the emergence of pests.

What is agricultural data?

Agricultural data is a subsection of Industry data. It can be used to understand crop production and to cater to the growing number of people in the world.

What is big data in smart farming?

In conclusion, Big Data is to provide predictive insights to future outcomes of farming (predictive yield model, predictive feed intake model, etc.), drive real-time operational decisions, and reinvent business processes for faster, innovative action and game-changing business models (Devlin, 2012).

How can IoT help in agriculture?

IoT in agriculture is designed to help farmers monitor vital information like humidity, air temperature and soil quality using remote sensors, and to improve yields, plan more efficient irrigation, and make harvest forecasts.

What is the connection between IoT big data and the cloud in agriculture?

The relationship between IoT, Big Data and Cloud Computing creates ample opportunity for business to harness exponential growth. Put simply, IoT is the source of data, Big Data is an analytic platform of data, and Cloud Computing is the location for storage, scale and speed of access.

Why is big data important for farmers?

Big data offers opportunities for smart and precise pesticides application, helping the farmer to easily make decisions on what pesticide to apply, when, and where.Such monitoring helps food producers to avoid the overuse of chemicals. Besides, it increases farmers’ profits by cutting costs on unnecessary pesticides use.

How does big data help the supply chain?

Big data makes it possible to achieve supply chain efficiency by offering tracking and optimization opportunities for delivery truck routes. As a result, food delivery cycles, from producer to the market, become much shorter, ensuring no food is wasted in the process.

How does remote management help farmers?

Thanks to big data applications that can process and analyze streams of data retrieved by a variety of sensors, ranging from satellites to farming equipment, farmers can remotely track their machinery in the field. This way they can eliminate all the unnecessary routes, considerably lowering spendings on fuel.

Why is smart farming important?

Ultimately, smart farming and precision agriculture practices help farmers to save costs and open new business opportunities. Here are the main possibilities that come with big data use in agribusinesses.

How does modern farming help?

And one of the tasks of modern farming is to enable instant detection of microbes and signs of contamination. This can be done by collecting data on temperature, humidity, and chemicals to assess the health of a growing plant.

What is happening in the agricultural sector in 2021?

by Analytics Insight January 7, 2021. Technological revolution that is currently happening in the agricultural sector became possible, among other things, due to big data. Collecting and analyzing big data can not only improve the productivity of individual farms but also help halt a global food crisis. The significance of this lies in the growing …

Is it hard to imagine the modern world without data?

It’s hard to imagine the modern world without data. More and more data is produced and used worldwide. But for the successful operation of an agricultural business , having an opportunity for big data analysis and management is key.

Why is big data important for agriculture?

Big data, when analyzed and layered together with other datasets within the data ecosystem, may help stakeholders in agriculture and nutri- tion to make better decisions across the entire food system . Although there are actions specific stakeholders can take towards making big data work for agriculture and nutrition, some actions are universal.

Why is it important to consider big data?

It is important to consider that big data fulfill s a specific role within the larger data ecosystem. The data ecosystem includes all sizes and types of datasets. Data may not become big data unless they are analyzed at a certain scale. Import- ance and impact of the dataset may not be correlated with the dataset size.

What is closed data?

The standard operating procedure of business, science, and management is closed data, meaning data that are not open or shared (ODI, 2015). If big data is to be used optimally, organizations need to share or open their data. However, this process may require them to change their busi- ness models, the people they hire, their business relationships, and their institutional culture. Such a process is slow and potentially threaten- ing to risk-averse organizations, or those that do not have the financial or human capacity to change. Researchers in universities are espe- cially averse to opening and sharing data, for fear of others stealing their results. However, they are open to reusing data that others have published (Digital Science, 2017). Other cul- tural considerations include bureaucracy and other social structures that impede data sharing, norms and structures that can be highly variable across countries or regions.

How does big data help the SDGs?

As the international community works to fulfill the SDGs, big data will drive many of the efforts tied to linking agriculture and nutrition and re- shaping the global food system. The collection of high-quality data is not sufficient. This vast well of information must translate into knowledge that is easily accessible by non-technical audiences, including policy-makers and civil society. By carefully building a system for open and big data, one that includes clear definitions, rules over ownership and use, and transparency and accountability, we can ensure that the benefits of big data are passed on to the most vulnerable segments of society.

What are the SDGs for food?

The amount of data collected on global food systems is immense, and the Sustainable Development Goals (SDGs) of the United Nations (UN) (especially SDGs 2, 3 and 17) encourage the sharing of information and data on agriculture and nutrition.

Where does big data come from?

While big data can be sourced from industry, academia, and government, it can also be generated by the users of farm equipment, mobile phones, and social media. When people use an app, the information they input and their behavior while using the app then becomes big data for others to interpret and use.

Do governments collect data?

Most governments across the world have minis- tries of agriculture, food, and health that collect and organize a tremendous amount of data. Governments are often the stewards of the data that they collect (Smith and Jellema, 2016), can own the data, and host it. Much of the data that exist across the world collected by governments may not be considered big data, especially within developing countries. However, governments have a responsibility to interpret big data and act upon it for the benefit of their citizens.

What are the goals of farmers?

To achieve each goal, farmers must make better decisions and move beyond the use of general knowledge from research experiments, which can only carry them so far.

What is yield prediction?

Some of the more prominent include: Yield prediction sees the use of mathematical models to analyse data around yield, weather, chemicals, leaf and biomass index among others, with machine learning used to crunch the stats and power the making of decisions.

What is big data in agriculture?

Big data in agriculture. Big data applications in agriculture are a combination of technology and analytics. It entails the collection, compilation, and timely processing of new data to help scientists and farmers make better and more informed decisions. Farming processes are increasingly becoming data-enabled and data-driven, …

What are the issues that are being addressed by big data applications in agriculture?

Sustainability, global food security, safety, and improved efficiency are some of the critical issues that are being addressed by big data applications in agriculture. Undoubtedly, these global issues have extended the scope of big data beyond farming and now cover the entire food supply chain. With the development of the Internet of Things, various components of agriculture and the supply chain are wirelessly connected, generating data that is accessible in real-time.

What is big data?

Big data is an extensive collection of both structured and unstructured data that can be mined for information and analyzed to build predictive systems for better decision making. Besides the government, telecom, healthcare, marketing, education, and several industrial sectors, big data applications in agriculture are gaining momentum as technologies like livestock monitoring gadgets, drones, and soil sensors are generating large volumes of data to support data-driven farming. The ultimate goal is to help farmers, agriculturists, and scientists adopt beneficial farming practices.

Why is big data not processed?

Due to complexity, big data cannot be processed by conventional data processing and data management applications and requires advanced tools that can analyze and process large volumes of data. Big data is characterized by some unique features – volume, variety, velocity, variability, veracity, and complexity. …

What is data influx?

Government – Data influx from sources such as sensors, satellites, CCTV and traffic cameras, calls, emails, social media, IT spaces, academia, etc. calls for efficient data storage and analysis for better governance and management of the public sector.

Why is big data important in agriculture?

That’s not surprising, given that data analytics has everything it takes to improve the operation of agriculture companies, and big data in agriculture is a vast source of knowledge and information that simply waits to be explored. That’s why agriculture companies more and more frequently use Big Data Services and Data Analytics Services.

How does big data affect agriculture?

With big data in agriculture, farmers can make sure they use precisely the right amount of these substances that violate any legal regulations or threaten the crops. On the other hand, the profitability of your farm goes up, as your plants and animals are free from weeds, insects, and various diseases.

Why do farmers use pesticides?

Farmers all over the world use pesticides and fertilizers to ensure that when the harvesting time comes , there will be plenty to reap.

Why do farmers use big data?

You see, farmers more and more frequently use big data in agriculture to assess weather and soil conditions (i.a. rainfalls, water usage, pesticide and fertilizer usage, optimal harvesting time, etc.) in order to get maximum productivity from the land they cultivate.

What is IoT used for?

Today, IoT devices are a more and more common sight on modern farmlands and catteries. IoT sensors can be used in: Tractors and loaders. Seeding equipment.

How can AI help in aviation?

In our article about AI in aviation, we told you about predictive maintenance. It’s a fascinating subpart of AI and data science that allows you to: 1 Keep your farming equipment in top quality 2 Maintain every vehicle and every machine according to manufacturer’s guidelines 3 Predict potential glitches and problems even before they occur

Is it safe to use pesticides on farms?

However, pesticides and fertilizers can be dangerous when not used properly, to the soil and to plants alike. That’s why effective but also ethical and safe usage of these chemicals is of paramount importance. Moreover, there are specific regulations and guidelines when it comes to chemicals used on farms.



Role of Big Data in Agriculture

  • Most people would wonder how any digital technology like big data can help farmers? According to McKinsey & Company, there is a global loss of almost $940 billion as about one-third of the food produced is wasted every year. Well-planned use of any technology can make life easier. Going by the same logic, big data experts suggest that farming processes can be made simpler …

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Benefits of Big Data in Agriculture

  • Almost five million people die of hunger in Africa every year. Similar is the problem with other developing and underdeveloped nations. A practical approach needs to be employed to deal with this problem than regular digital technologies. This is where big data can help with real-time data analytics and automated processing. Farmers can now be more precise in the practices they foll…

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Use Cases of Big Data in Agriculture

  • Big data has plenty of use cases within farming; we’ve just scratched the surface. Its ability to analyze data in real-time, predict weather patterns, and suggest the best practices can revolutionize the farming industry. Let’s take a look at some of them: 1. Monitoring Natural Factors – The output of the farmers’ efforts often depends upon uncontr…

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  • That’s what big data has in store for the agriculture industry. It helps farmers make better and smarter decisions; revamping today’s agricultural economy. Although the major focus is on improving processes in order to make more profits, an added advantage is that it will give food to billions of people around the world.

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